Background: With the pandemic of Coronavirus Disease 2019, differing case-fatality rates and limited resources have led to adoption of separate management strategies for severe and nonsevere disease. For patients in quarantine in the community, self-assessment of COVID-19 severity risk can guide appropriate medical consultation. Methods: Data from 45,450 patients infected with COVID-19 from January 1 to February 27, 2020 were extracted from the municipal Notifiable Disease Report System in Wuhan, China. T-test and chi-square test were used to investigate the associations of various patient characteristics with disease severity, and multivariable logistic regression models identified strongly correlated variables for inclusion in the scale. Scale accuracy was assessed using receiver operating characteristic analysis. A least absolute shrinkage and selection operator regression cross-validated prediction accuracy.Results: Twelve scale items - age, gender, illness duration, dyspnea, shortness of breath (clinical evidence of altered breathing), hypertension, pulmonary disease, diabetes, cardio/cerebrovascular disease, number of comorbidities, neutrophil percentage, and lymphocyte percentage - were identified and showed good predictive ability (area under the curve =0·72). After excluding the community healthcare laboratory parameters, the remaining model (the final self-assessment scale) showed similar area under the curve (=0·71).Conclusions: Our COVID-19 severity self-assessment scale can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance. The tool is also practical for use in preliminary screening in community healthcare settings.